ABSTRACT. This paper presents a contribution to the large problematic of integrating medical image-based information into a structured framework (such as ...
1 Whilst we use the term Web system throughout this paper, we recognise that this terminology is ... Another significant impact pertains to e-business interfaces.
Oct 11, 2010 - Very often, we use multi-dimensional perceptual and cognitive spaces, such .... along various modality dimensions so as to allow for multi-modal generation of output. ..... has been made (âs6â): ... galaxy.
Similarly, development paradigms for information systems do not typically take into ... developing multimedia applications - multimedia authoring - is essentially ...
A computational framework to represent nutrient utilization for body protein and lipid accretion by growing monogastric animals is presented. Nutrient and ...
at (February 2006): http://nltk.sourceforge.net/tech/index.html. 4. Brill, E.: A report of recent progress in transformation-based error-driven learning. In: Proc.
Introduction. The ability of analogy making is considered to be an essen- tial part of intelligent behavior. By mapping concepts and knowledge about a ...
This work presents an extensible framework for the storage and exchange of ..... a network interface, although for testing purposes the schema remains relatively simple .... [18] and a vari- ant of the SAX [21] API, cElementtree [7], in Python [19].
QIPING SHEN*, HENG LI, JACKY CHUNG and PUI-YEE HUI. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom,.
ering and studying network performance information. This work presents an extensible framework for the storage and exchange of performance measurements.
Abstract. In this paper the problems of organization and representation of concept knowledge are addressed from an autonomous agent perspective. The first ...
In this paper, we present a first approach to the representation of patent .... A patent family encompasses all patents that belong to the same invention.1 Vari-.
sequences from KDDI and MERL. Since there are eight cameras in the test set, seven depth maps are acquired for the first frame using the stereo matching.
In figure 3, E is an adopted child of N in T and the sequence. B,N,E is a generalized .... [Dung,Kowalski,Toni,2006] Phan Minh Dung, Robert A. Kowalski, and ...
In mobile healthcare, medical information are often expressed in different formats due to the local policies and regulations and the heterogeneity of the ...
of the perceptual color space to illustrate a more structured set of qual- ...... Kuhn T. (1970): The Structure of Scientific Revolutions, University of Chicago. Press ...
Oct 17, 2005 - We analyze the needs of three mobile healthcare stakeholders to capture the .... healthcare call centers (also called healthcare portals), and.
for a large number of rules of the form: (fname) â Andrew,. (fname) â John, and .... cess input records with a single attribute (plain strings), and parsing of the ...
the data grid object in the prototype's main interface under the âCoordinateâ table at the bottom of the UI. It take
This paper proposes a framework for representing cross-lingual/interlingual lexical ... pseudo synset, which is introduced to represent a cross-lingual/multilingual ...
Figure 1 shows five example author records derived from a publication database .... described using context-free grammar rules, that specifies how input records ...
Figure 5: A Program in our framework for processing names. 1. Andrew. M. 2. John. M. 3. Mary. F. 4 ... ... 1. Alex. Alexander. M. 2. Andy. Anderson. M. 3. Andy.
Sep 14, 2017 - detection and description: a survey. DMKD, 29(3):626â688,. 2015. [6] M. Al Hasan and M. J. Zaki. A survey of link prediction in social networks.
H. Greenspan, J. Goldberger, and A. Mayer meaningful video-objects that may be useful for later indexing and retrieval applications. Video has both spatial and ...
GRAFIP framework. Objectives: Extraction, structuration and integration of information from pathological cerebral images into a generic model of the human ...
GRAFIP: a Framework for the Representation of Healthy and Pathological Anatomical and Functional Cerebral Information C. Hudelot, J. Atif, O. Nempont, B. Batrancourt, E. Angelini and I. Bloch ENST-GET, Dept TSI, CNRS UMR 5141 LTCI, Paris, France
Context Context
GRAFIP GRAFIP framework framework
Importance of the integration of image-based information in medical information systems. Structured representation of image content: related to generic medical knowledge, numerical information specific to individual patients. GRAFIP : Graph(s) of Representation of Anatomical and Functional data for Individual patients including Pathologies.
Objectives: Extraction, structuration and integration of information from pathological cerebral images into a generic model of the human brain. Methodology: Modeling human brain generic knowledge (anatomical, functional and pathological). Instantiation of the generic model on image data. Knowledge-based image segmentation [4]. GRAFIP updating via integration of image-based information.
Generic Generic human human brain brain model model Anatomical knowledge: (1) “neuraxis part” of the Foundational Model of Anatomy [1] + (2) Neuranat complex spatial relations between cerebral structures [2]. Pathological knowledge: brain tumor ontology: taxonomical relations (tumor classification), related findings, structural descriptions of pathological structures. Linked to anatomical knowledge through an has_anotomical_location relation. Functional knowledge: description of functional activity areas, in particular Brodmann areas. Linked to anatomical knowledge (on the Cerebral cortex) through an is_part_of relation.
Overview of the GRAFIP framework
GRAFIP: GRAFIP: aa graph graph based based representation representation Node representation of brain structures with multiple viewpoints [3]: semantic viewpoint: semantic medical interpretation, spatial viewpoint: spatial description and spatial relations, perceptual viewpoint: visual appearance in images and numerical description. Hypergraph structure to manage complex relations with cardinality >2 (e.g. between, anatomo-functional relations). Patient GRAFIP initialized with a generic healthy anatomical model. GRAFIP built up and updated by a collaborative process between image segmentation and knowledge-based reasoning. GRAFIP structure
Conclusion Conclusion An original framework for cerebral information representation: Combines generic knowledge representation and specific patient information extracted from medical images. A pathology-dependent paradigm. A better understanding of pathological impacts on surrounding structures and on functional brain organization. Future works: longitudinal GRAFIP studies, GRAFIP-based case comparison, EPR formatting.
References References [1] http://sig.biostr.washington.edu/projects/fm/AboutFM.html [2] http://www.chups.jussieu.fr/ext/neuranat/ [3] O. Dameron, B. Gibaud and X. Morandi : Numeric and symbolic representation of the cerebral cortex anatomy: methods and preliminary results. Surg. Rad .Ana. 26(3), 2004 [4] O. Colliot, O. Camara and I. Bloch: Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation. Pattern Recognition, 2006.
